Analysis Overview
Sarcoma (Primary solid tumor)
17 October 2017  |  None
Maintainer Information
Maintained by Broad Institute GDAC (Broad Institute of MIT & Harvard)
Overview
Introduction

This is an overview of Sarcoma analysis pipelines from FireCloud run "17 October 2017".

Summary

Note: These results are offered to the community as an additional reference point, enabling a wide range of cancer biologists, clinical investigators, and genome and computational scientists to easily incorporate TCGA into the backdrop of ongoing research. While every effort is made to ensure that FireCloud input data and algorithms are of the highest possible quality, these analyses have not been reviewed by domain experts.

Results
  • Sequence and Copy Number Analyses

    • Analysis of mutagenesis by APOBEC cytidine deaminases (P-MACD).
      View Report | There are 236 tumor samples in this analysis. The Benjamini-Hochberg-corrected p-value for enrichment of the APOBEC mutation signature in 6 samples is <=0.05. Out of these, 2 have enrichment values >2, which implies that in such samples at least 50% of APOBEC signature mutations have been in fact made by APOBEC enzyme(s).

    • Mutation Assessor
      View Report | 

    • Mutation Signature Analysis
      View Report | Our analysis idenfied 1 solution(s) of mutational signatures across 236 samples by BayesNMF method.

    • SNP6 Copy number analysis (GISTIC2)
      View Report | There were 260 tumor samples used in this analysis: 36 significant arm-level results, 24 significant focal amplifications, and 36 significant focal deletions were found.

  • Correlations to Clinical Parameters

    • Correlation between aggregated molecular cancer subtypes and selected clinical features
      View Report | Testing the association between subtypes identified by 10 different clustering approaches and 9 clinical features across 261 patients, 59 significant findings detected with P value < 0.05 and Q value < 0.25.

    • Correlation between APOBEC groups and selected clinical features
      View Report | Testing the association between APOBEC groups identified by 2 different apobec score and 9 clinical features across 236 patients, one significant finding detected with P value < 0.05 and Q value < 0.25.

    • Correlation between APOBEC signature variables and clinical features
      View Report | Testing the association between 3 variables and 9 clinical features across 236 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 4 clinical features related to at least one variables.

    • Correlation between copy number variation genes (focal events) and selected clinical features
      View Report | Testing the association between copy number variation 60 focal events and 9 clinical features across 260 patients, 174 significant findings detected with Q value < 0.25.

    • Correlation between copy number variations of arm-level result and selected clinical features
      View Report | Testing the association between copy number variation 82 arm-level events and 9 clinical features across 260 patients, 61 significant findings detected with Q value < 0.25.

    • Correlation between gene mutation status and selected clinical features
      View Report | Testing the association between mutation status of 5 genes and 9 clinical features across 236 patients, 8 significant findings detected with Q value < 0.25.

    • Correlation between mutation rate and clinical features
      View Report | Testing the association between 2 variables and 10 clinical features across 236 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 4 clinical features related to at least one variables.

  • Clustering Analyses

    • Clustering of copy number data by focal peak region with absolute value: consensus NMF
      View Report | The most robust consensus NMF clustering of 260 samples using the 60 most variable genes was identified for k = 3 clusters. We computed the clustering for k = 2 to k = 10 and used the cophenetic correlation coefficient and the average silhouette width calculation to determine the robust clusters.

    • Clustering of copy number data by peak region with threshold value: consensus NMF
      View Report | The most robust consensus NMF clustering of 260 samples using the 60 most variable genes was identified for k = 4 clusters. We computed the clustering for k = 2 to k = 10 and used the cophenetic correlation coefficient and the average silhouette width calculation to determine the robust clusters.

    • Clustering of lincRNA expression: consensus hierarchical
      View Report | Median absolute deviation (MAD) was used to select 2500 most variable lincRNAs. Consensus ward linkage hierarchical clustering of 258 samples and 2500 lincRNAs identified 5 subtypes with the stability of the clustering increasing for k = 2 to k = 10.

    • Clustering of lincRNA expression: consensus NMF
      View Report | The most robust consensus NMF clustering of 259 samples using the 2500 most variable lincRNAs was identified for k = 3 clusters. We computed the clustering for k = 2 to k = 10 and used the cophenetic correlation coefficient and the average silhouette width calculation to determine the robust clusters.

    • Clustering of miR mature expression: consensus hierarchical
      View Report | Median absolute deviation (MAD) was used to select 273 most variable miRs. Consensus ward linkage hierarchical clustering of 258 samples and 273 miRs identified 3 subtypes with the stability of the clustering increasing for k = 2 to k = 10.

    • Clustering of miR mature expression: consensus NMF
      View Report | The most robust consensus NMF clustering of 259 samples using the 273 most variable miRs was identified for k = 4 clusters. We computed the clustering for k = 2 to k = 10 and used the cophenetic correlation coefficient and the average silhouette width calculation to determine the robust clusters.

    • Clustering of protein coding gene expression: consensus NMF
      View Report | The most robust consensus NMF clustering of 259 samples using the 2500 most variable genes was identified for k = 3 clusters. We computed the clustering for k = 2 to k = 10 and used the cophenetic correlation coefficient and the average silhouette width calculation to determine the robust clusters.

    • Clustering of Protein-coding gene expression: consensus hierarchical
      View Report | Median absolute deviation (MAD) was used to select 2500 most variable genes. Consensus ward linkage hierarchical clustering of 258 samples and 2500 genes identified 6 subtypes with the stability of the clustering increasing for k = 2 to k = 10.

  • Other Analyses

    • Identification of putative miR direct targets by sequencing data
      View Report | The CLR algorithm was applied on 766 miRs and 18776 mRNAs across 257 samples. After 2 filtering steps, the number of 129 miR:gene pairs were detected.

    • Methylation__HM450_Clustering_CNMF
      View Report | The most robust consensus NMF clustering of 261 samples using the 17546 most variable genes was identified for k = 7 clusters. We computed the clustering for k = 2 to k = 10 and used the cophenetic correlation coefficient and the average silhouette width calculation to determine the robust clusters.

    • Methylation__HM450_Clustering_Consensus_Plus
      View Report | Median absolute deviation (MAD) was used to select 2500 most variable genes. Consensus ward linkage hierarchical clustering of 260 samples and 2500 genes identified 4 subtypes with the stability of the clustering increasing for k = 2 to k = 10.

    • Mutation_MutSig2CV
      View Report | 

  • Other Correlation Analyses

    • Correlation between copy number variation genes (focal events) and molecular subtypes
      View Report | Testing the association between copy number variation 60 focal events and 10 molecular subtypes across 260 patients, 444 significant findings detected with P value < 0.05 and Q value < 0.25.

    • Correlation between copy number variations of arm-level result and molecular subtypes
      View Report | Testing the association between copy number variation 82 arm-level events and 10 molecular subtypes across 260 patients, 390 significant findings detected with P value < 0.05 and Q value < 0.25.

    • Correlation between gene mutation status and molecular subtypes
      View Report | Testing the association between mutation status of 5 genes and 10 molecular subtypes across 236 patients, 29 significant findings detected with P value < 0.05 and Q value < 0.25.

Methods & Data
Input
  • Summary Report Date = Thu Dec 14 14:08:01 2017

  • Protection = FALSE